Delta Sharing + MinIO AIStor enables direct access to on-prem data from Databricks without data duplication. Organizations can now query their hybrid infrastructure through a single interface, eliminating storage costs and synchronization complexity.
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A major financial institution turned their "outdated and escalating" Hadoop costs into savings by combining AIStor + Dremio on existing hardware. The hybrid approach delivered cloud elasticity while reusing their infrastructure, proving you don't need to rip-and-replace everything to modernize
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The "big data king" that enterprises spent billions on is dying. Not from lack of trying, but because cloud-native alternatives now beat it at its own game. AIStor is faster and cheaper than HDFS. The revolution is happening in quarters, not decades.
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Liberating data enables government agencies to modernize storage, cut hidden costs, and unlock mission-driven insights. By strategically curating, archiving, and aligning data with AI-driven priorities, agencies improve efficiency, compliance, and decision-making while preparing for the future.
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Apache Iceberg 1.9.0 closes the gap with Delta Lake, adding row-level ops with lineage, JSON-friendly variant type, geospatial support, REST catalog upgrades, and easier Delta migration. Dropping Hadoop 2 and Spark 3.3 signals a modern focus, driving open table format convergence.
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What is a Sovereign Cloud? Look up the definition of “sovereignty” in any dictionary, and you will get a definition along the lines of “supreme power or authority.” So, a logical definition of “Sovereign Cloud” would be a cloud where a single governing entity like the European Union or the government of a single country can create acts and policies
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Object storage is the primary storage solution for OLAP databases. This survey highlights major database players that have embraced this movement.
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In today's data-driven world, object storage has emerged as the foundation for modern workloads including artificial intelligence (AI), machine learning (ML), and data lakehouse analytics. This is evidenced by the fact that all the major Large Language Models—OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, etc.—are all built on object stores. And as organizations continue
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Anyone who has worked in a team environment knows that every successful team has one go-to person—that special individual who can help you regardless of the nature of your problem. On a traditional software development team, this individual is an expert programmer and is also an expert in one other technology, which could be a database technology like Snowflake
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OpenAI’s move this week to release two new open-weight AI models (gpt-oss-120b and gpt-oss-20b) just changed Enterprise Data Infrastructure forever. The news has rightfully made ripples across the tech ecosystem. Why? * Because these models are released under the Apache 2.0 license, users can for the first time in 5 years, run OpenAI models right on their own devices.
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Enterprises are moving AI and analytics workloads off public clouds to cut costs and regain control without sacrificing performance. Cloud repatriation brings cloud-native design on-prem.
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SELinux will try to tag all files in the filesystem, causing the pod start to be delayed until all files are tagged, often when the PVC has a bigger amount of fies this will cause a timeout and the minio container will not even start.
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The data lake was once heralded as the future, an infinitely scalable reservoir for all our raw data, promising to transform it into actionable insights. This was a logical progression from databases and data warehouses, each step driven by the increasing demand for scalability. Yet, in embracing the data lake's scale and flexibility, we overlooked a critical difference.
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When it comes to storing and managing data, there is the modern, cloud-native way and there is the traditional, appliance oriented way. Needless to say, object storage is the modern, cloud-native way. While it would be simple to suggest that MinIO is hopelessly biased towards object storage (it is, afterall, the only thing we do), that would miss a key
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To support AI and analytics, a data lakehouse must be secure by design. This blog covers best practices for securing storage, metadata, and catalog layers including encryption, fine-grained IAM, audit logging, object locking, and multi-site replication without sacrificing performance.
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MinIO, the leader in high-performance AI storage, has once again raised the bar in the AI infrastructure industry with its groundbreaking MinIO AIStor platform. Leveraging next-generation AMD hardware, KIOXIA NVMe™ SSDs, and cutting-edge software optimizations, MinIO AIStor delivers unmatched performance, scalability, and efficiency for AI-driven and other data intensive workloads. Today, we are excited to share benchmark results that demonstrate
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It is hard to believe that it was 13 years ago that Marc Andressen penned his famous blog entitled “Software is Eating the World.” In it he spoke of the disruption that modern software organizations were inflicting on traditional businesses. Thirteen years later, even in the face of stratospheric valuations for Nvidia, software continues to eat the world. The evidence
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Ransomware attacks are nothing new. The first ransomware attack occurred 36 years ago in 1989, and it is known as the AIDS Trojan PC Cyborg Virus. Floppy disks infected with a Trojan virus were mailed to attendees of the World Health Organization’s AIDS conference and other individuals. The virus waited until the computer had been booted 90 times. Then
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MLOps, short for Machine Learning Operations, is a set of practices and tools aimed at addressing the specific needs of engineers building models and moving them into production. Some organizations start off with a few homegrown tools that version datasets after each experiment and checkpoint models after every epoch of training. On the other hand, many organizations have chosen to
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An abbreviated version of this post appeared on The New Stack on March 19th, 2024. In enterprise artificial intelligence, there are two main types of models: discriminative and generative. Discriminative models are used to classify or predict data, while generative models are used to create new data. Even though Generative AI has dominated the news of late, organizations are still
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